1. Field of the Invention
The present invention relates to a method for controlling a game character, and more particularly, to a method for recognizing a character position in each mode of a game and determining a character position in the next mode. In addition, the present invention relates to a method for automatically recognizing a current game situation of a game such as a group sports game, a strategy/simulation game, and a casual game where a computer controls one or more game characters, and character positions play a crucial role in the game, and controlling behaviors of the individual characters on the basis of the recognized game situation.
2. Description of the Related Art
In the case of a soccer game, game situations include (1) a normal offense situation, (2) a shoot chance situation, (3) a normal defense situation, (4) a situation where an opponent is about to gain a point. The situations may be defined arbitrarily by a user and be mapped to each output node.
To play the aforementioned game, data of each independent characters is prepared. If a user cannot prepare character data, then a character already prepared in a game program is used. In this case, the problem is that characters prepared in the game program are limited, and can hardly catch up with changes of the game. To solve this problem, there have been attempts to use an artificial intelligence technology in forming a game character. The artificial intelligence in a computer game makes characters of the game look like real people, and thus a user can be more absorbed in the game.
The attempts to use the artificial intelligence technology to form game characters include a finite state machine (FSM), a fuzzy state machine (FuSM), and artificial life (Alife).
The Alife is a field of study that examines general characteristics of life through simulations using artificial medium such as computers or robots. The Alife compliments the traditional analytic approach of biology on biological phenomena with a synthetic approach. Although game developers have tried to apply the Alife technology to a game for a long time, the Alife technology was only limitedly applied to the field of game because of its unpredictability. Recently, an attempt to apply the Alife to games has been increasingly made because once basic characteristics of the Alife including flexibility and creativity are applied to a game, the game can be more interesting because of the flexibility to a complex environment and user's manipulation, or an unexpected creative behavior. However, the study for the Alife application in the game field is focused on mapping out overall strategy of a game where characters are clustered, and still remains at a basic level.
The FSM is a rule-based system, which is being most widely used. In the FSM, a finite number of states are connected in a graph controlled by transition between states. Since the FSM can be implemented with just ‘if-else’ or ‘switch-case’ statements, the FSM is widely used and can be easily understood and implemented as a program. For example, when finding an opponent in a moving state, a character transits the moving state to a chase state to chase the opponent. Once the opponent enters a predetermined distance, the characters transits the state to an attack state to attack the opponent. Hence, as mentioned above, the FSM is advantageous in that its implementation is easy and an behavior of a character can be easily defined. However, the FSM has disadvantages in that if a game of an opponent employs the FSM, a user may easily predict a game play pattern of the opponent after a predetermined time since the game begun. The predictability lowers an interest in game. To overcome the disadvantages, the FuSM is used.
In the case of the FuSM integrating the FSM with a fuzzy theory, a fuzzy function is applied to input and output values to allow random operations to a certain extent. The randomness makes it difficult for the user to predict a behavior of an opponent since there are possibilities for the opponent to take different behaviors under the same circumstance. However, the implementation of the FSM and the FuSM is easy only when the number of character states is small. If the number of states increases, organizing state diagrams becomes difficult, and a program becomes rapidly complicated. Also, to add a new behavior pattern, both FSM and FuSM must be undesirably newly programmed.
As mentioned above, the conventional artificial intelligence technology is mostly for games such as a board game that requires recognition of an control over an entire game situation. Hence, a game developer must previously define and design every situation and perform coding for every situation. Also, the game designed in such a manner must be coded again whenever a situation or a game rule is changed, because of its inability to recognize and adapt to a new environment. Moreover, the complicated artificial intelligence technology such as the Alife cannot be easily used for an actual game because of its excessively large calculation amount.